import pandas as pd
import numpy as np
from scipy.io import loadmat
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import tensorflow as tf
from sklearn import svm
from sklearn.model_selection import train_test_split

Excercise 1

data = loadmat('ex6/ex6data1.mat')

X, y = data['X'], data['y']
df = pd.DataFrame(np.append(X, y, axis=1))
df.columns = ['x1', 'x2', 'y']

fig1 = px.scatter(df, x='x1', y='x2', color='y', symbol='y')
fig1.show()

# Linear kernerl SVM with different regularization C parameters
clf_c_1 = svm.SVC(C=1, kernel='linear', probability=True,)
clf_c_100 = svm.SVC(C=100.00, kernel='linear', probability=True)

clf_c_1.fit(df[['x1', 'x2']], df['y'])
clf_c_100.fit(df[['x1', 'x2']], df['y'])

SVC(C=100.0, kernel='linear', probability=True)

cord = np.linspace(0, 5, 100)
z_1 = np.zeros((cord.size, cord.size))
z_100 = np.zeros((cord.size, cord.size))
# Evaluate z = over grid
for i, ji in enumerate(cord):
    for ii, kii in enumerate(cord):
        z_1[i, ii] = clf_c_1.predict(np.array([[kii, ji]]))
        z_100[i, ii] = clf_c_100.predict(np.array([[kii, ji]]))

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IOPub data rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_data_rate_limit`.

Current values:
NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)
NotebookApp.rate_limit_window=3.0 (secs)

fig2 = go.Figure(fig1)
fig2.add_trace(
    go.Contour(
        z=z_1,
        x=cord,
        y=cord,
        contours=dict(
            start=0,
            end=5,
            size=10,
        ),
        contours_coloring='lines',
        line_width=2,
        colorscale='Greens',
        name='C=1 SVM boundry'
    )
)
fig2.add_trace(
    go.Contour(
        z=z_100,
        x=cord,
        y=cord,
        contours=dict(
            start=0,
            end=5,
            size=10,
        ),
        contours_coloring='lines',
        line_width=2,
        colorscale='Reds',
        name='C=100 SVM boundry'
    )
)
fig2.update_layout(legend=dict(
    orientation="h",
    yanchor="bottom",
    y=1.02,
    xanchor="right",
    x=1
))
fig2.update_layout(coloraxis_showscale=False)
fig2.show()

Excercise 2

data = loadmat('ex6/ex6data2.mat')

X, y = data['X'], data['y']
df = pd.DataFrame(np.append(X, y, axis=1))
df.columns = ['x1', 'x2', 'y']

fig1 = px.scatter(df, x='x1', y='x2', color='y', symbol='y')
fig1.update_layout(coloraxis_showscale=False)
fig1.show()

# Gaussian kernerl SVM with different regularization C parameters
clf_c_1 = svm.SVC(C=1, kernel='rbf')
clf_c_100 = svm.SVC(C=1000.00, kernel='rbf')

clf_c_1.fit(df[['x1', 'x2']], df['y'])
clf_c_100.fit(df[['x1', 'x2']], df['y'])

SVC(C=1000.0)

df.describe()

	x1	x2	y
count	863.000000	863.000000	863.000000
mean	0.503414	0.694304	0.556199
std	0.254967	0.158884	0.497120
min	0.044931	0.402632	0.000000
25%	0.292627	0.557018	0.000000
50%	0.512673	0.693713	1.000000
75%	0.699309	0.830409	1.000000
max	0.998848	0.988596	1.000000

x1_cord = np.linspace(0, 1, 100)
x2_cord = np.linspace(0.4, 1, 100)
z_1 = np.zeros((x1_cord.size, x2_cord.size))
z_100 = np.zeros((x1_cord.size, x2_cord.size))
# Evaluate z = over grid
for i, ji in enumerate(x2_cord):
    for ii, kii in enumerate(x1_cord):
        z_1[i, ii] = clf_c_1.predict(np.array([[kii, ji]]))
        z_100[i, ii] = clf_c_100.predict(np.array([[kii, ji]]))

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C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

C:\Users\juliu\anaconda3\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but SVC was fitted with feature names

fig2 = go.Figure(fig1)
fig2.add_trace(
    go.Contour(
        z=z_100,
        x=x1_cord,
        y=x2_cord,
        contours=dict(
            start=0,
            end=5,
            size=10,
        ),
        contours_coloring='lines',
        line_width=2,
        colorscale='Greens',
        name='C=1 SVM boundry'
    )
)
fig2.update_layout(legend=dict(
    orientation="h",
    yanchor="bottom",
    y=1.02,
    xanchor="right",
    x=1
))
fig2.update_layout(coloraxis_showscale=False)
fig2.show()

Excercise 3

data = loadmat('ex6/ex6data3.mat')

X, y = data['X'], data['y']
df = pd.DataFrame(np.append(X, y, axis=1))
df.columns = ['x1', 'x2', 'y']

fig1 = px.scatter(df, x='x1', y='x2', color='y', symbol='y')
fig1.update_layout(coloraxis_showscale=False)
fig1.show()

# Gaussian kernerl SVM with different regularization C parameters
clf_c_1 = svm.SVC(C=1, kernel='rbf')
clf_c_100 = svm.SVC(C=100.00, kernel='rbf')

clf_c_1.fit(df[['x1', 'x2']].to_numpy(), df['y'].to_numpy())
clf_c_100.fit(df[['x1', 'x2']].to_numpy(), df['y'].to_numpy())
# To avoid future predicting warning:
# X does not have valid feature names,but SVC was fitted with feature names

SVC(C=100.0)

df.describe()

	x1	x2	y
count	211.000000	211.000000	211.000000
mean	-0.130246	-0.059743	0.502370
std	0.200516	0.314751	0.501183
min	-0.596774	-0.657895	0.000000
25%	-0.287903	-0.313889	0.000000
50%	-0.130300	-0.052924	1.000000
75%	0.013825	0.192690	1.000000
max	0.297235	0.573392	1.000000

x1_cord = np.linspace(-0.7, 0.6, 100)
x2_cord = np.linspace(-0.7, 0.6, 100)
z_1 = np.zeros((x1_cord.size, x2_cord.size))
z_100 = np.zeros((x1_cord.size, x2_cord.size))
# Evaluate z = over grid
for i, ji in enumerate(x2_cord):
    for ii, kii in enumerate(x1_cord):
        z_1[i, ii] = clf_c_1.predict(np.array([[kii, ji]]))
        z_100[i, ii] = clf_c_100.predict(np.array([[kii, ji]]))

fig2 = go.Figure(fig1)
fig2.add_trace(
    go.Contour(
        z=z_1,
        x=x1_cord,
        y=x2_cord,
        contours=dict(
            start=0,
            end=5,
            size=10,
        ),
        contours_coloring='lines',
        line_width=2,
        colorscale='Greens',
        name='C=1 SVM boundry'
    )
)
fig2.add_trace(
    go.Contour(
        z=z_100,
        x=x1_cord,
        y=x2_cord,
        contours=dict(
            start=0,
            end=5,
            size=10,
        ),
        contours_coloring='lines',
        line_width=2,
        colorscale='Reds',
        name='C=100 SVM boundry'
    )
)
fig2.update_layout(legend=dict(
    orientation="h",
    yanchor="bottom",
    y=1.02,
    xanchor="right",
    x=1
))
fig2.update_layout(coloraxis_showscale=False)
fig2.show()